Data Engineer - Senior Consultant USA

As a Senior Data Engineer - Consultant at Provectus, you'll be responsible for leading the technical implementation of enterprise-scale data solutions. Your role will involve driving architecture decisions, establishing technical standards, and mentoring junior engineers. You'll also build strong relationships with clients, acting as a trusted technical advisor throughout various engagements.

To apply for the Senior Data Engineer - Consultant position at Provectus, candidates should have 5-8 years of data engineering experience, a strong knowledge of AWS, and expertise in programming languages such as Python, Java, or Scala. A Bachelor's degree in Computer Science, Engineering, or a related field is required, while a Master's degree is preferable.

The ideal Senior Data Engineer - Consultant at Provectus will showcase strong leadership capabilities and consulting skills. Additional expertise in multiple cloud platforms, sound knowledge of data modeling and governance frameworks, and a background in agile development methodologies will be invaluable. Experience in mentoring junior engineers will also set you apart from other candidates.

At Provectus, Senior Data Engineers - Consultants will work with advanced cloud technologies, primarily AWS, and big data technologies. You will also leverage programming languages like Python, Java, or Scala in cloud-based environments to develop and implement innovative data solutions.

As a Senior Data Engineer - Consultant at Provectus, you’ll find numerous opportunities for professional growth. This includes a clear path to becoming a Solution Architect, access to professional development resources, certification support, and exposure to a range of technical challenges across diverse industries.

When answering this question, select a project that demonstrates your technical skills and adaptability. Discuss your role, the technologies used, challenges faced, and how you contributed to the project's success.

A thoughtful answer will include discussing processes such as automated testing, validation checks, and routine audits. Mention specific tools or methodologies you use to maintain high data quality.

Detail your hands-on experience with AWS services relevant to data engineering, such as S3, Redshift, or Glue. Provide examples of how you've used these services in previous projects to achieve results.

Share your mentoring philosophy, including specific strategies you implement to help juniors grow, such as regular check-ins, pairing on tasks, or organizing learning sessions.

Choose a specific example where you encountered a challenging technical issue. Break down your thought process, the steps you took to resolve it, and the outcome to showcase your problem-solving skills.

Mention your commitment to continuous learning through online courses, webinars, or tech meetups. Discuss how you apply new knowledge to your work, emphasizing your proactive nature.

Discuss specific big data technologies you've worked with, such as Hadoop, Spark, or Kafka. Provide insights into projects where you applied these technologies, explaining their impact on the outcome.

Employ a structured approach, such as using task management tools or agile methodologies, explaining how you assess priorities based on deadlines, complexity, and resource availability.

Highlight your communication strategies, tools used for collaboration (like Slack or Jira), and techniques for promoting team bonding and inclusivity to create a supportive work culture.

Share your vision for the future, including trends like automation in data pipelines, increased use of AI and machine learning, and the impact of real-time data processing on business intelligence.